Dependency structure matrix analysis: Offline utility of the dependency structure matrix genetic algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3103, Issue , 2004, Pages 355-366

  Yu, Tian Li ^a   Goldberg, David E ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS;

CROSSOVER OPERATOR; DEPENDENCY STRUCTURE MATRIXES; IS DESIGN; OFFLINE;

ALGORITHMS;

EID: 35048825262     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24855-2_31     Document Type: Article

References (13)

1. Yu, T.-L., Goldberg, D.E., Yassine, A., Chen, Y.-p.: Genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm. Proceedings of Artificial Neural Networks in Engineering 2003 (ANNIE 2003) (2003) 327-332 (Also IlliGAL Report No. 2003007).
2. Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI (1975)
3. Goldberg, D.E.: The design of innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Boston, MA (2002)
4. Steward, D.V.: The design structure system: A method for managing the design of complex systems. IEEE Transactions on Engineering Management 28 (1981) 77-74
5. Yassine, A., Falkenburg, D.R., Chelst, K.: Engineering design management: An information structure approach. International Journal of production research 37 (1999) 2957-2975
6. Yu, T.-L., Yassine, A., Goldberg, D.E.: A genetic algorithm for developing modular product architectures. Proceedings of the ASME 2003 International Design Engineering Technical Conferences, 15th International Conference on Design Theory and Methodology (DETC 2003) (2003) DTM-48657 (Also IlliGAL Report No. 2003024).
7. Pelikan, M., Goldberg, D.E., Lobo, F.G.: A survey of optimization by building and using probabilistic models. IlliGAL Report No. 99018, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL (1999)
8. Larrañaga, P., Lozano, J.A., eds.: Estimation of Distribution Algorithms. Kluwer Academic Publishers (2002)
9. Sharman, D., Yassine, A., Carlile, P.: Characterizing modular architectures. ASME 14th International Conference, DTM-34024 (2002)
10. Munetomo, M., Goldberg, D.E.: Identifying linkage groups by nonlinearity/nonmonotonicity detection. Proceedings of the Genetic and Evolutionary Computation Conference 1999: Volume 1 (1999) 433-440
11. Thierens, D., Goldberg, D.E.: Convergence models of genetic algorithm selection schemes. In: Parallel Problem Solving from Nature, PPSN III. (1994) 119-129
12. Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3 (1989) 493-530
13. Kargupta, H.: The performance of the gene expression messy genetic algorithm on real test functions. Proceedings of 1996 IEEE International Conference on Evolutionary Computation (1996) 631-636